Merge pull request #437 from HyunjunA/infvisfrontendmlbackend

test plot_tsne

machine/learn/skl_utils.py

@@ -1088,7 +1088,7 @@ def plot_imp_score(tmpdir, _id, coefs, feature_names, imp_score_type):
     plt.yticks(range(num_bar), feature_names[indices])
     plt.ylim([-1, num_bar])
     h.tight_layout()
-    plt.savefig(tmpdir + _id + '/imp_score_' + _id + '.png')
+    # plt.savefig(tmpdir + _id + '/imp_score_' + _id + '.png')
     plt.close()
     return top_features, indices
 
@@ -1146,6 +1146,11 @@ def plot_learning_curve(tmpdir,_id,model,features,target,cv,return_times=True):
     # test_scores_mean = np.mean(test_scores, axis=1)
     # test_scores_std = np.std(test_scores, axis=1)
 
+
+    print('train_sizes.tolist()',train_sizes.tolist())
+    print('train_scores', train_scores.tolist())
+    print('test_scores', test_scores.tolist())
+
     learning_curve_dict = {
         'train_sizes':train_sizes.tolist(),
         'train_scores': train_scores.tolist(),
@@ -1170,32 +1175,22 @@ def plot_pca_2d(tmpdir,_id,features,target):
 
     # np.random.seed(5)
 
-    # iris = datasets.load_iris()
-    # print(features)
+
     X = np.array(features)
     y = np.array(target)
 
-    print(set(y))
+    # print(set(y))
 
 
 
 
 
     plt.cla()
     pca = decomposition.PCA(n_components=2)
-    # pca = decomposition.PCA(n_components=2)
     pca.fit(X)
     X = pca.transform(X)
 
-    # plt.scatter(x,y, c = z, cmap = mcolors.ListedColormap(["black", "green"]))
-
-    # plt.show()
 
-
-    # version 1 
-    # colors = np.array(["black", "green"])
-    # plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1, edgecolor='k')
-
 
 
     # version 2
@@ -1204,13 +1199,10 @@ def plot_pca_2d(tmpdir,_id,features,target):
     colors = plt.cm.Set1(np.linspace(0, 1, num_classes))
 
     plt.scatter(X[:, 0], X[:, 1], c=y, cmap=mcolors.ListedColormap(colors))
-    # plot the legend where the colors are mapped to the classes
+
     plt.legend(handles=[Patch(color=colors[i], label="class_"+str(i)) for i in range(num_classes)])
 
-    # cb = plt.colorbar()
-    # loc = np.arange(0,max(label),max(label)/float(len(colors)))
-    # cb.set_ticks(loc)
-    # cb.set_ticklabels(colors)
+
 
 
 
@@ -1222,6 +1214,10 @@ def plot_pca_2d(tmpdir,_id,features,target):
 
 
 
+    plt.close()
+
+
+
 
     # print("X")
     # print(X)
@@ -1493,7 +1489,7 @@ def plot_tsne(tmpdir,_id,features,target):
 
     # plt.show()
     # plt.savefig(tmpdir + _id + '/tsne_' + _id + '.png')
-    # plt.close()
+    plt.close()
 
 
 

tests/integration/jest/runExperiment_classificaion.test.ts

@@ -22,7 +22,8 @@ describe('run experiment', () => {
 	it('run LogisticRegression experiment on banana', async () => {
 		console.log('run LogisticRegression experiment on banana')
 
-		jest.setTimeout(util.JEST_TIMEOUT)
+		// jest.setTimeout(util.JEST_TIMEOUT)
+		jest.setTimeout(util.JEST_TIMEOUT*10)
 		let algoName = 'LogisticRegression'
 		let algoParms = {
 			"penalty": "l1",
@@ -102,7 +103,8 @@ describe('run experiment', () => {
 	it('run decisionTree experiment with invalid parms on banana', async () => {
 		console.log('run decisionTree experiment with invalid parms on banana')
 
-		jest.setTimeout(util.JEST_TIMEOUT)
+		// jest.setTimeout(util.JEST_TIMEOUT)
+		jest.setTimeout(util.JEST_TIMEOUT*2)
 
 		let algoName = 'LogisticRegression'
 		let algoParms = {
@@ -130,6 +132,8 @@ describe('run experiment', () => {
 
 	 	algoParms.dataset = datasetId
 
+		console.log("datasetId",datasetId)
+
 	 	// fetch previously run experiments
 		var prevExperiments = await labApi.fetchExperiments()